Healthcare Insurance Cost Classification Using Machine Learning's Linear Regression Approach
Kanwarpartap Singh Gill, Rupesh Gupta, Mukesh Kumar, Ruchira Rawat, Yerrolla Chanti · 2024
Advanced analytical approaches are required for effective prediction and categorization of healthcare expenses because of the increasing complexity of these costs and the dynamic nature of the costs themselves. Within the scope of this research project, the classification of healthcare insurance costs is investigated via the use of machine learning, more especially linear regression. In order to identify correlations between these characteristics and to forecast insurance costs, the linear regression model makes use of a dataset that contains a wide variety of demographic, medical, and financial information. The ultimate goal of this study is to provide assistance to policymakers, insurers, and healthcare providers in optimising resource allocation and increasing cost-effectiveness within the healthcare sector. The increasing intricacies and ever-changing nature of healthcare expenses need sophisticated analytical techniques to provide precise forecasting and categorization. This work investigates the use of machine learning, particularly linear regression, for categorising healthcare insurance expenses. The linear regression model utilises a dataset that includes a wide range of demographic, medical, and financial characteristics. Its objective is to identify connections between these elements and make predictions about insurance costs. The study is centred on feature selection, model training, and performance assessment in order to improve the accuracy and interpretability of the predictive model. This research aims to use linear regression to give valuable insights into the determinants of healthcare insurance costs. The findings will be beneficial for policymakers, insurers, and healthcare providers as they can use this information to allocate resources more efficiently and enhance cost-effectiveness in the healthcare industry.